Add Kernel Inception Distance (KID) metric for model evaluation #9151

Is your feature request related to a problem? Please describe.
FIDMetric and MMDMetric are provided for evaluating models, but no Kernel Inception Distance (KID). KID is widely reported alongside FID in image synthesis and image-to-image translation papers (papers like CUT, UNSB, Contourdiff). It is unbiased and more reliable than FID with small sample sizes, which is common in medical imaging test sets.

Describe the solution you'd like
A KIDMetric in monai/metrics/kid.py following the same design as FIDMetric:

• Takes pre-extracted feature vectors (N x F) for generated and real images, so any feature extractor (ImageNet, RadImageNet, MedicalNet) can be used.
• Computes the unbiased MMD^2 with a polynomial kernel k(x, y) = (x·y / d + 1)^3.
• Unit tests checking known values, input validation, and agreement with a reference implementation.

Describe alternatives you've considered
Using torchmetrics' KernelInceptionDistance, but it bundles an Inception feature extractor, while MONAI's FIDMetric works on arbitrary features, which suits medical images better.

Additional context
Reference: Bińkowski et al., "Demystifying MMD GANs", ICLR 2018.
I'm happy to implement this and open a PR if maintainers agree.